SOTAVerified

Protein Design

Formally, given the design requirements of users, models are required to generate protein amino acid sequences that align with those requirements.

Papers

Showing 41–50 of 175 papers

TitleStatusHype
Diffusion Models for Constrained DomainsCode1
Generative De Novo Protein Design with Global ContextCode1
Controllable Protein Sequence Generation with LLM Preference OptimizationCode1
Diffusion Sequence Models for Enhanced Protein Representation and GenerationCode1
Context-Guided Diffusion for Out-of-Distribution Molecular and Protein DesignCode1
Geometric Trajectory Diffusion ModelsCode1
Generating Novel, Designable, and Diverse Protein Structures by Equivariantly Diffusing Oriented Residue CloudsCode1
Fast non-autoregressive inverse folding with discrete diffusionCode1
Improving few-shot learning-based protein engineering with evolutionary samplingCode1
Peptide-GPT: Generative Design of Peptides using Generative Pre-trained Transformers and Bio-informatic SupervisionCode1
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1GraphTransPerplexity6.63—Unverified
2StructGNNPerplexity6.4—Unverified
3AlphaDesignPerplexity6.3—Unverified
4GCAPerplexity6.05—Unverified
5GVPPerplexity5.36—Unverified
6ProteinMPNNPerplexity4.61—Unverified
7PiFoldPerplexity4.55—Unverified
8Knowledge-DesignPerplexity3.46—Unverified
#ModelMetricClaimedVerifiedStatus
1ESM-IFPerplexity6.44—Unverified
2GVP-largePerplexity6.17—Unverified